{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LANfactory` package is a light-weight convenience package for training `likelihood approximation networks` (LANs) in torch (or keras), \n",
    "starting from supplied training data.\n",
    "\n",
    "[LANs](https://elifesciences.org/articles/65074), although more general in potential scope of applications, were conceived in the context of sequential sampling modeling\n",
    "to account for cognitive processes giving rise to *choice* and *reaction time* data in *n-alternative forced choice experiments* commonly encountered in the cognitive sciences.\n",
    "\n",
    "In this quick tutorial we will use the [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package to generate our training data using such a sequential sampling model (SSM). The use of of the `LANfactory` package is in no way bound to utilize this `ssms` package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install\n",
    "\n",
    "To install the `ssms` package type,\n",
    "\n",
    "`pip install git+https://github.com/AlexanderFengler/ssm_simulators`\n",
    "\n",
    "To install the `LANfactory` package type,\n",
    "\n",
    "`pip install git+https://github.com/AlexanderFengler/LANfactory`\n",
    "\n",
    "Necessary dependency should be installed automatically in the process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary packages\n",
    "import ssms\n",
    "import lanfactory\n",
    "import os\n",
    "import numpy as np\n",
    "from copy import deepcopy\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate Training Data\n",
    "First we need to generate some training data. As mentioned above we will do so using the `ssms` python package, however without delving into a detailed explanation\n",
    "of this package. Please refer to the [basic ssms tutorial] (https://github.com/AlexanderFengler/ssm_simulators) in case you want to learn more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAKE CONFIGS\n",
    "model = \"ddm\"\n",
    "# Initialize the generator config (for MLP LANs)\n",
    "generator_config = deepcopy(ssms.config.data_generator_config[\"lan\"])\n",
    "# Specify generative model (one from the list of included models mentioned above)\n",
    "generator_config[\"model\"] = model\n",
    "# Specify number of parameter sets to simulate\n",
    "generator_config[\"n_parameter_sets\"] = 10000\n",
    "# Specify how many samples a simulation run should entail\n",
    "generator_config[\"n_samples\"] = 2000\n",
    "# Specify folder in which to save generated data\n",
    "generator_config[\"output_folder\"] = \"data/lan_mlp/\" + model + \"/\"\n",
    "\n",
    "# Make model config dict\n",
    "model_config = ssms.config.model_config[model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cpus used:  12\n",
      "checking:  data/lan_mlp/ddm/\n",
      "0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# MAKE DATA\n",
    "\n",
    "my_dataset_generator = ssms.dataset_generators.lan_mlp.data_generator(\n",
    "    generator_config=generator_config, model_config=model_config\n",
    ")\n",
    "\n",
    "for i in range(25):\n",
    "    print(i)\n",
    "    training_data = my_dataset_generator.generate_data_training_uniform(save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(25):\n",
    "    print(i)\n",
    "    training_data = my_dataset_generator.generate_data_training_uniform(save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dataset completed'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare for Training\n",
    "\n",
    "Next we set up dataloaders for training with pytorch. The `LANfactory` uses custom dataloaders, taking into account particularities of the expected training data.\n",
    "Specifically, we expect to receive a bunch of training data files (the present example generates only one), where each file hosts a large number of training examples. \n",
    "So we want to define a dataloader which spits out batches from data with a specific training data file, and keeps checking when to load in a new file. \n",
    "The way this is implemented here, is via the `DatasetTorch` class in `lanfactory.trainers`, which inherits from `torch.utils.data.Dataset` and prespecifies a `batch_size`. Finally this is supplied to a [`DataLoader`](https://pytorch.org/docs/stable/data.html), for which we keep the `batch_size` argument at 0.\n",
    "\n",
    "The `DatasetTorch` class is then called as an iterator via the DataLoader and takes care of batching as well as file loading internally. \n",
    "\n",
    "You may choose your own way of defining the `DataLoader` classes, downstream you are simply expected to supply one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAKE DATALOADERS\n",
    "\n",
    "# List of datafiles (here only one)\n",
    "folder_ = \"data/lan_mlp/\" + model + \"/\"  # + \"/training_data_0_nbins_0_n_1000/\"\n",
    "file_list_ = [folder_ + file_ for file_ in os.listdir(folder_)]\n",
    "\n",
    "# Training dataset\n",
    "torch_training_dataset = lanfactory.trainers.DatasetTorch(\n",
    "    file_ids=file_list_,\n",
    "    batch_size=1024,\n",
    "    features_key=\"lan_data\",\n",
    "    label_key=\"lan_labels\",\n",
    ")\n",
    "\n",
    "torch_training_dataloader = torch.utils.data.DataLoader(\n",
    "    torch_training_dataset,\n",
    "    shuffle=True,\n",
    "    batch_size=None,\n",
    "    num_workers=1,\n",
    "    pin_memory=True,\n",
    ")\n",
    "\n",
    "# Validation dataset\n",
    "torch_validation_dataset = lanfactory.trainers.DatasetTorch(\n",
    "    file_ids=file_list_,\n",
    "    batch_size=1024,\n",
    "    features_key=\"lan_data\",\n",
    "    label_key=\"lan_labels\",\n",
    ")\n",
    "\n",
    "torch_validation_dataloader = torch.utils.data.DataLoader(\n",
    "    torch_validation_dataset,\n",
    "    shuffle=True,\n",
    "    batch_size=None,\n",
    "    num_workers=1,\n",
    "    pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define two configuration dictionariers,\n",
    "\n",
    "1. The `network_config` dictionary defines the architecture and properties of the network\n",
    "2. The `train_config` dictionary defines properties concerning training hyperparameters\n",
    "\n",
    "Two examples (which we take as provided by the package, but which you can adjust according to your needs) are provided below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network config: \n",
      "{'layer_sizes': [100, 100, 100, 1], 'activations': ['tanh', 'tanh', 'tanh', 'linear'], 'train_output_type': 'logprob'}\n",
      "Train config: \n",
      "{'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 5, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': True}\n"
     ]
    }
   ],
   "source": [
    "# SPECIFY NETWORK CONFIGS AND TRAINING CONFIGS\n",
    "\n",
    "network_config = deepcopy(lanfactory.config.network_configs.network_config_mlp)\n",
    "network_config[\"layer_sizes\"] = [100, 100, 100, 1]\n",
    "network_config[\"activations\"] = [\"tanh\", \"tanh\", \"tanh\", \"linear\"]\n",
    "\n",
    "print(\"Network config: \")\n",
    "print(network_config)\n",
    "\n",
    "train_config = deepcopy(lanfactory.config.network_configs.train_config_mlp)\n",
    "\n",
    "print(\"Train config: \")\n",
    "print(train_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now load a network, and save the configuration files for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tanh\n",
      "tanh\n",
      "tanh\n",
      "linear\n",
      "Found folder:  data\n",
      "Moving on...\n",
      "Found folder:  data/torch_models\n",
      "Moving on...\n",
      "Found folder:  data/torch_models/angle\n",
      "Moving on...\n",
      "Saved network config\n",
      "Saved train config\n"
     ]
    }
   ],
   "source": [
    "# LOAD NETWORK\n",
    "net = lanfactory.trainers.TorchMLP(\n",
    "    network_config=deepcopy(network_config),\n",
    "    input_shape=torch_training_dataset.input_dim,\n",
    "    save_folder=\"/data/torch_models/\",\n",
    "    generative_model_id=\"angle\",\n",
    ")\n",
    "\n",
    "# SAVE CONFIGS\n",
    "lanfactory.utils.save_configs(\n",
    "    model_id=model + \"_torch_\",\n",
    "    save_folder=\"data/torch_models/angle/\",\n",
    "    network_config=network_config,\n",
    "    train_config=train_config,\n",
    "    allow_abs_path_folder_generation=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finally train the network we supply our network, the dataloaders and training config to the `ModelTrainerTorchMLP` class, from `lanfactory.trainers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch Device:  cpu\n",
      "Found folder:  data\n",
      "Moving on...\n",
      "Found folder:  data/torch_models\n",
      "Moving on...\n",
      "Found folder:  data/torch_models/ddm_lan\n",
      "Moving on...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "passing 1\n",
      "wandb not available\n",
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     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# TRAIN MODEL\n",
    "model_trainer = lanfactory.trainers.ModelTrainerTorchMLP(\n",
    "    model=net,\n",
    "    train_config=train_config,\n",
    "    train_dl=torch_training_dataloader,\n",
    "    valid_dl=torch_validation_dataloader,\n",
    "    allow_abs_path_folder_generation=False,\n",
    "    pin_memory=True,\n",
    "    seed=None,\n",
    ")\n",
    "\n",
    "# model_trainer.train_model(save_history=True, save_model=True, verbose=0)\n",
    "model_trainer.train_and_evaluate(\n",
    "    wandb_on=False,\n",
    "    output_folder=\"data/torch_models/\" + model + \"_lan\" + \"/\",\n",
    "    output_file_id=model,\n",
    ")\n",
    "# LOAD MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Model for Inference and Call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LANfactory` provides some convenience functions to use networks for inference after training. \n",
    "We can load a model using the `LoadTorchMLPInfer` class, which then allows us to run fast inference via either\n",
    "a direct call, which expects a `torch.tensor` as input, or the `predict_on_batch` method, which expects a `numpy.array` \n",
    "of `dtype`, `np.float32`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tanh\n",
      "tanh\n",
      "tanh\n",
      "linear\n"
     ]
    }
   ],
   "source": [
    "network_path_list = os.listdir(\"data/torch_models/\" + model + \"_lan\")\n",
    "network_file_path = [\n",
    "    \"data/torch_models/\" + model + \"_lan/\" + file_\n",
    "    for file_ in network_path_list\n",
    "    if \"state_dict\" in file_\n",
    "][0]\n",
    "\n",
    "network = lanfactory.trainers.LoadTorchMLPInfer(\n",
    "    model_file_path=network_file_path,\n",
    "    network_config=network_config,\n",
    "    input_dim=torch_training_dataset.input_dim,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "direct call out:  tensor([-66.8203])\n",
      "predict_on_batch out:  [-66.82027]\n"
     ]
    }
   ],
   "source": [
    "# Two ways to call the network\n",
    "\n",
    "# Direct call --> need tensor input\n",
    "direct_out = network(\n",
    "    torch.from_numpy(np.array([1, 1.5, 0.5, 1.0, 0.1, 0.65, 1.0], dtype=np.float32))\n",
    ")\n",
    "print(\"direct call out: \", direct_out)\n",
    "\n",
    "# predict_on_batch method\n",
    "predict_on_batch_out = network.predict_on_batch(\n",
    "    np.array([1, 1.5, 0.5, 1.0, 0.1, 0.65, 1.0], dtype=np.float32)\n",
    ")\n",
    "print(\"predict_on_batch out: \", predict_on_batch_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A peek into the first passage distribution computed by the network\n",
    "\n",
    "We can compare the learned likelihood function in our `network` with simulation data from the underlying generative model.\n",
    "For this purpose we recruit the [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_37965/289158266.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"rt\"].iloc[:1000] = np.linspace(5, 0, 1000)\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_37965/289158266.py:13: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[5.         4.99499499 4.98998999 4.98498498 4.97997998 4.97497497\n",
      " 4.96996997 4.96496496 4.95995996 4.95495495 4.94994995 4.94494494\n",
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      " 4.03903904 4.03403403 4.02902903 4.02402402 4.01901902 4.01401401\n",
      " 4.00900901 4.004004   3.998999   3.99399399 3.98898899 3.98398398\n",
      " 3.97897898 3.97397397 3.96896897 3.96396396 3.95895896 3.95395395\n",
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      " 3.91891892 3.91391391 3.90890891 3.9039039  3.8988989  3.89389389\n",
      " 3.88888889 3.88388388 3.87887888 3.87387387 3.86886887 3.86386386\n",
      " 3.85885886 3.85385385 3.84884885 3.84384384 3.83883884 3.83383383\n",
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      " 3.76876877 3.76376376 3.75875876 3.75375375 3.74874875 3.74374374\n",
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      " 3.67867868 3.67367367 3.66866867 3.66366366 3.65865866 3.65365365\n",
      " 3.64864865 3.64364364 3.63863864 3.63363363 3.62862863 3.62362362\n",
      " 3.61861862 3.61361361 3.60860861 3.6036036  3.5985986  3.59359359\n",
      " 3.58858859 3.58358358 3.57857858 3.57357357 3.56856857 3.56356356\n",
      " 3.55855856 3.55355355 3.54854855 3.54354354 3.53853854 3.53353353\n",
      " 3.52852853 3.52352352 3.51851852 3.51351351 3.50850851 3.5035035\n",
      " 3.4984985  3.49349349 3.48848849 3.48348348 3.47847848 3.47347347\n",
      " 3.46846847 3.46346346 3.45845846 3.45345345 3.44844845 3.44344344\n",
      " 3.43843844 3.43343343 3.42842843 3.42342342 3.41841842 3.41341341\n",
      " 3.40840841 3.4034034  3.3983984  3.39339339 3.38838839 3.38338338\n",
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      " 3.31831832 3.31331331 3.30830831 3.3033033  3.2982983  3.29329329\n",
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      " 3.04804805 3.04304304 3.03803804 3.03303303 3.02802803 3.02302302\n",
      " 3.01801802 3.01301301 3.00800801 3.003003   2.997998   2.99299299\n",
      " 2.98798799 2.98298298 2.97797798 2.97297297 2.96796797 2.96296296\n",
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      " 2.83783784 2.83283283 2.82782783 2.82282282 2.81781782 2.81281281\n",
      " 2.80780781 2.8028028  2.7977978  2.79279279 2.78778779 2.78278278\n",
      " 2.77777778 2.77277277 2.76776777 2.76276276 2.75775776 2.75275275\n",
      " 2.74774775 2.74274274 2.73773774 2.73273273 2.72772773 2.72272272\n",
      " 2.71771772 2.71271271 2.70770771 2.7027027  2.6976977  2.69269269\n",
      " 2.68768769 2.68268268 2.67767768 2.67267267 2.66766767 2.66266266\n",
      " 2.65765766 2.65265265 2.64764765 2.64264264 2.63763764 2.63263263\n",
      " 2.62762763 2.62262262 2.61761762 2.61261261 2.60760761 2.6026026\n",
      " 2.5975976  2.59259259 2.58758759 2.58258258 2.57757758 2.57257257\n",
      " 2.56756757 2.56256256 2.55755756 2.55255255 2.54754755 2.54254254\n",
      " 2.53753754 2.53253253 2.52752753 2.52252252 2.51751752 2.51251251\n",
      " 2.50750751 2.5025025  2.4974975  2.49249249 2.48748749 2.48248248\n",
      " 2.47747748 2.47247247 2.46746747 2.46246246 2.45745746 2.45245245\n",
      " 2.44744745 2.44244244 2.43743744 2.43243243 2.42742743 2.42242242\n",
      " 2.41741742 2.41241241 2.40740741 2.4024024  2.3973974  2.39239239\n",
      " 2.38738739 2.38238238 2.37737738 2.37237237 2.36736737 2.36236236\n",
      " 2.35735736 2.35235235 2.34734735 2.34234234 2.33733734 2.33233233\n",
      " 2.32732733 2.32232232 2.31731732 2.31231231 2.30730731 2.3023023\n",
      " 2.2972973  2.29229229 2.28728729 2.28228228 2.27727728 2.27227227\n",
      " 2.26726727 2.26226226 2.25725726 2.25225225 2.24724725 2.24224224\n",
      " 2.23723724 2.23223223 2.22722723 2.22222222 2.21721722 2.21221221\n",
      " 2.20720721 2.2022022  2.1971972  2.19219219 2.18718719 2.18218218\n",
      " 2.17717718 2.17217217 2.16716717 2.16216216 2.15715716 2.15215215\n",
      " 2.14714715 2.14214214 2.13713714 2.13213213 2.12712713 2.12212212\n",
      " 2.11711712 2.11211211 2.10710711 2.1021021  2.0970971  2.09209209\n",
      " 2.08708709 2.08208208 2.07707708 2.07207207 2.06706707 2.06206206\n",
      " 2.05705706 2.05205205 2.04704705 2.04204204 2.03703704 2.03203203\n",
      " 2.02702703 2.02202202 2.01701702 2.01201201 2.00700701 2.002002\n",
      " 1.996997   1.99199199 1.98698699 1.98198198 1.97697698 1.97197197\n",
      " 1.96696697 1.96196196 1.95695696 1.95195195 1.94694695 1.94194194\n",
      " 1.93693694 1.93193193 1.92692693 1.92192192 1.91691692 1.91191191\n",
      " 1.90690691 1.9019019  1.8968969  1.89189189 1.88688689 1.88188188\n",
      " 1.87687688 1.87187187 1.86686687 1.86186186 1.85685686 1.85185185\n",
      " 1.84684685 1.84184184 1.83683684 1.83183183 1.82682683 1.82182182\n",
      " 1.81681682 1.81181181 1.80680681 1.8018018  1.7967968  1.79179179\n",
      " 1.78678679 1.78178178 1.77677678 1.77177177 1.76676677 1.76176176\n",
      " 1.75675676 1.75175175 1.74674675 1.74174174 1.73673674 1.73173173\n",
      " 1.72672673 1.72172172 1.71671672 1.71171171 1.70670671 1.7017017\n",
      " 1.6966967  1.69169169 1.68668669 1.68168168 1.67667668 1.67167167\n",
      " 1.66666667 1.66166166 1.65665666 1.65165165 1.64664665 1.64164164\n",
      " 1.63663664 1.63163163 1.62662663 1.62162162 1.61661662 1.61161161\n",
      " 1.60660661 1.6016016  1.5965966  1.59159159 1.58658659 1.58158158\n",
      " 1.57657658 1.57157157 1.56656657 1.56156156 1.55655656 1.55155155\n",
      " 1.54654655 1.54154154 1.53653654 1.53153153 1.52652653 1.52152152\n",
      " 1.51651652 1.51151151 1.50650651 1.5015015  1.4964965  1.49149149\n",
      " 1.48648649 1.48148148 1.47647648 1.47147147 1.46646647 1.46146146\n",
      " 1.45645646 1.45145145 1.44644645 1.44144144 1.43643644 1.43143143\n",
      " 1.42642643 1.42142142 1.41641642 1.41141141 1.40640641 1.4014014\n",
      " 1.3963964  1.39139139 1.38638639 1.38138138 1.37637638 1.37137137\n",
      " 1.36636637 1.36136136 1.35635636 1.35135135 1.34634635 1.34134134\n",
      " 1.33633634 1.33133133 1.32632633 1.32132132 1.31631632 1.31131131\n",
      " 1.30630631 1.3013013  1.2962963  1.29129129 1.28628629 1.28128128\n",
      " 1.27627628 1.27127127 1.26626627 1.26126126 1.25625626 1.25125125\n",
      " 1.24624625 1.24124124 1.23623624 1.23123123 1.22622623 1.22122122\n",
      " 1.21621622 1.21121121 1.20620621 1.2012012  1.1961962  1.19119119\n",
      " 1.18618619 1.18118118 1.17617618 1.17117117 1.16616617 1.16116116\n",
      " 1.15615616 1.15115115 1.14614615 1.14114114 1.13613614 1.13113113\n",
      " 1.12612613 1.12112112 1.11611612 1.11111111 1.10610611 1.1011011\n",
      " 1.0960961  1.09109109 1.08608609 1.08108108 1.07607608 1.07107107\n",
      " 1.06606607 1.06106106 1.05605606 1.05105105 1.04604605 1.04104104\n",
      " 1.03603604 1.03103103 1.02602603 1.02102102 1.01601602 1.01101101\n",
      " 1.00600601 1.001001   0.995996   0.99099099 0.98598599 0.98098098\n",
      " 0.97597598 0.97097097 0.96596597 0.96096096 0.95595596 0.95095095\n",
      " 0.94594595 0.94094094 0.93593594 0.93093093 0.92592593 0.92092092\n",
      " 0.91591592 0.91091091 0.90590591 0.9009009  0.8958959  0.89089089\n",
      " 0.88588589 0.88088088 0.87587588 0.87087087 0.86586587 0.86086086\n",
      " 0.85585586 0.85085085 0.84584585 0.84084084 0.83583584 0.83083083\n",
      " 0.82582583 0.82082082 0.81581582 0.81081081 0.80580581 0.8008008\n",
      " 0.7957958  0.79079079 0.78578579 0.78078078 0.77577578 0.77077077\n",
      " 0.76576577 0.76076076 0.75575576 0.75075075 0.74574575 0.74074074\n",
      " 0.73573574 0.73073073 0.72572573 0.72072072 0.71571572 0.71071071\n",
      " 0.70570571 0.7007007  0.6956957  0.69069069 0.68568569 0.68068068\n",
      " 0.67567568 0.67067067 0.66566567 0.66066066 0.65565566 0.65065065\n",
      " 0.64564565 0.64064064 0.63563564 0.63063063 0.62562563 0.62062062\n",
      " 0.61561562 0.61061061 0.60560561 0.6006006  0.5955956  0.59059059\n",
      " 0.58558559 0.58058058 0.57557558 0.57057057 0.56556557 0.56056056\n",
      " 0.55555556 0.55055055 0.54554555 0.54054054 0.53553554 0.53053053\n",
      " 0.52552553 0.52052052 0.51551552 0.51051051 0.50550551 0.5005005\n",
      " 0.4954955  0.49049049 0.48548549 0.48048048 0.47547548 0.47047047\n",
      " 0.46546547 0.46046046 0.45545546 0.45045045 0.44544545 0.44044044\n",
      " 0.43543544 0.43043043 0.42542543 0.42042042 0.41541542 0.41041041\n",
      " 0.40540541 0.4004004  0.3953954  0.39039039 0.38538539 0.38038038\n",
      " 0.37537538 0.37037037 0.36536537 0.36036036 0.35535536 0.35035035\n",
      " 0.34534535 0.34034034 0.33533534 0.33033033 0.32532533 0.32032032\n",
      " 0.31531532 0.31031031 0.30530531 0.3003003  0.2952953  0.29029029\n",
      " 0.28528529 0.28028028 0.27527528 0.27027027 0.26526527 0.26026026\n",
      " 0.25525526 0.25025025 0.24524525 0.24024024 0.23523524 0.23023023\n",
      " 0.22522523 0.22022022 0.21521522 0.21021021 0.20520521 0.2002002\n",
      " 0.1951952  0.19019019 0.18518519 0.18018018 0.17517518 0.17017017\n",
      " 0.16516517 0.16016016 0.15515516 0.15015015 0.14514515 0.14014014\n",
      " 0.13513514 0.13013013 0.12512513 0.12012012 0.11511512 0.11011011\n",
      " 0.10510511 0.1001001  0.0950951  0.09009009 0.08508509 0.08008008\n",
      " 0.07507508 0.07007007 0.06506507 0.06006006 0.05505506 0.05005005\n",
      " 0.04504505 0.04004004 0.03503504 0.03003003 0.02502503 0.02002002\n",
      " 0.01501502 0.01001001 0.00500501 0.        ]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
      "  data[\"rt\"].iloc[:1000] = np.linspace(5, 0, 1000)\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_37965/289158266.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"choice\"].iloc[:1000] = -1\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_37965/289158266.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"choice\"].iloc[1000:] = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.zeros((2000, 7), dtype=np.float32),\n",
    "    columns=[\"v\", \"a\", \"z\", \"t\", \"theta\", \"rt\", \"choice\"],\n",
    ")\n",
    "data[\"v\"] = 0.0\n",
    "data[\"a\"] = 1.25\n",
    "data[\"z\"] = 0.5\n",
    "data[\"t\"] = 0.2\n",
    "data[\"theta\"] = 0.0\n",
    "data[\"rt\"].iloc[:1000] = np.linspace(5, 0, 1000)\n",
    "data[\"rt\"].iloc[1000:] = np.linspace(0, 5, 1000)\n",
    "data[\"choice\"].iloc[:1000] = -1\n",
    "data[\"choice\"].iloc[1000:] = 1\n",
    "\n",
    "# Network predictions\n",
    "predict_on_batch_out = network.predict_on_batch(data.values.astype(np.float32))\n",
    "\n",
    "# Simulations\n",
    "from ssms.basic_simulators.simulator import simulator\n",
    "\n",
    "sim_out = {}\n",
    "for i in range(10):\n",
    "    print(i)\n",
    "    sim_out[i] = simulator(model=model, theta=data.values[0, :-2], n_samples=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'likelihod')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot network predictions\n",
    "plt.plot(\n",
    "    data[\"rt\"] * data[\"choice\"],\n",
    "    np.exp(predict_on_batch_out),\n",
    "    color=\"black\",\n",
    "    label=\"network\",\n",
    ")\n",
    "\n",
    "# Plot simulations\n",
    "for i in range(10):\n",
    "    my_seed = np.random.choice(1000000)\n",
    "    sim_out = simulator(\n",
    "        model=model, theta=data.values[0, :-2], n_samples=2000, random_state=my_seed\n",
    "    )\n",
    "    plt.hist(\n",
    "        sim_out[\"rts\"] * sim_out[\"choices\"],\n",
    "        bins=100,\n",
    "        histtype=\"step\",\n",
    "        label=\"simulations\" if i == 0 else None,\n",
    "        color=\"blue\",\n",
    "        alpha=0.2,\n",
    "        density=True,\n",
    "    )\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"SSM likelihood\")\n",
    "plt.xlabel(\"rt\")\n",
    "plt.ylabel(\"likelihod\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_25238/1241343318.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"rt\"].iloc[:1000] = np.linspace(20, 0, 1000)\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_25238/1241343318.py:13: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[20.         19.97997998 19.95995996 19.93993994 19.91991992 19.8998999\n",
      " 19.87987988 19.85985986 19.83983984 19.81981982 19.7997998  19.77977978\n",
      " 19.75975976 19.73973974 19.71971972 19.6996997  19.67967968 19.65965966\n",
      " 19.63963964 19.61961962 19.5995996  19.57957958 19.55955956 19.53953954\n",
      " 19.51951952 19.4994995  19.47947948 19.45945946 19.43943944 19.41941942\n",
      " 19.3993994  19.37937938 19.35935936 19.33933934 19.31931932 19.2992993\n",
      " 19.27927928 19.25925926 19.23923924 19.21921922 19.1991992  19.17917918\n",
      " 19.15915916 19.13913914 19.11911912 19.0990991  19.07907908 19.05905906\n",
      " 19.03903904 19.01901902 18.998999   18.97897898 18.95895896 18.93893894\n",
      " 18.91891892 18.8988989  18.87887888 18.85885886 18.83883884 18.81881882\n",
      " 18.7987988  18.77877878 18.75875876 18.73873874 18.71871872 18.6986987\n",
      " 18.67867868 18.65865866 18.63863864 18.61861862 18.5985986  18.57857858\n",
      " 18.55855856 18.53853854 18.51851852 18.4984985  18.47847848 18.45845846\n",
      " 18.43843844 18.41841842 18.3983984  18.37837838 18.35835836 18.33833834\n",
      " 18.31831832 18.2982983  18.27827828 18.25825826 18.23823824 18.21821822\n",
      " 18.1981982  18.17817818 18.15815816 18.13813814 18.11811812 18.0980981\n",
      " 18.07807808 18.05805806 18.03803804 18.01801802 17.997998   17.97797798\n",
      " 17.95795796 17.93793794 17.91791792 17.8978979  17.87787788 17.85785786\n",
      " 17.83783784 17.81781782 17.7977978  17.77777778 17.75775776 17.73773774\n",
      " 17.71771772 17.6976977  17.67767768 17.65765766 17.63763764 17.61761762\n",
      " 17.5975976  17.57757758 17.55755756 17.53753754 17.51751752 17.4974975\n",
      " 17.47747748 17.45745746 17.43743744 17.41741742 17.3973974  17.37737738\n",
      " 17.35735736 17.33733734 17.31731732 17.2972973  17.27727728 17.25725726\n",
      " 17.23723724 17.21721722 17.1971972  17.17717718 17.15715716 17.13713714\n",
      " 17.11711712 17.0970971  17.07707708 17.05705706 17.03703704 17.01701702\n",
      " 16.996997   16.97697698 16.95695696 16.93693694 16.91691692 16.8968969\n",
      " 16.87687688 16.85685686 16.83683684 16.81681682 16.7967968  16.77677678\n",
      " 16.75675676 16.73673674 16.71671672 16.6966967  16.67667668 16.65665666\n",
      " 16.63663664 16.61661662 16.5965966  16.57657658 16.55655656 16.53653654\n",
      " 16.51651652 16.4964965  16.47647648 16.45645646 16.43643644 16.41641642\n",
      " 16.3963964  16.37637638 16.35635636 16.33633634 16.31631632 16.2962963\n",
      " 16.27627628 16.25625626 16.23623624 16.21621622 16.1961962  16.17617618\n",
      " 16.15615616 16.13613614 16.11611612 16.0960961  16.07607608 16.05605606\n",
      " 16.03603604 16.01601602 15.995996   15.97597598 15.95595596 15.93593594\n",
      " 15.91591592 15.8958959  15.87587588 15.85585586 15.83583584 15.81581582\n",
      " 15.7957958  15.77577578 15.75575576 15.73573574 15.71571572 15.6956957\n",
      " 15.67567568 15.65565566 15.63563564 15.61561562 15.5955956  15.57557558\n",
      " 15.55555556 15.53553554 15.51551552 15.4954955  15.47547548 15.45545546\n",
      " 15.43543544 15.41541542 15.3953954  15.37537538 15.35535536 15.33533534\n",
      " 15.31531532 15.2952953  15.27527528 15.25525526 15.23523524 15.21521522\n",
      " 15.1951952  15.17517518 15.15515516 15.13513514 15.11511512 15.0950951\n",
      " 15.07507508 15.05505506 15.03503504 15.01501502 14.99499499 14.97497497\n",
      " 14.95495495 14.93493493 14.91491491 14.89489489 14.87487487 14.85485485\n",
      " 14.83483483 14.81481481 14.79479479 14.77477477 14.75475475 14.73473473\n",
      " 14.71471471 14.69469469 14.67467467 14.65465465 14.63463463 14.61461461\n",
      " 14.59459459 14.57457457 14.55455455 14.53453453 14.51451451 14.49449449\n",
      " 14.47447447 14.45445445 14.43443443 14.41441441 14.39439439 14.37437437\n",
      " 14.35435435 14.33433433 14.31431431 14.29429429 14.27427427 14.25425425\n",
      " 14.23423423 14.21421421 14.19419419 14.17417417 14.15415415 14.13413413\n",
      " 14.11411411 14.09409409 14.07407407 14.05405405 14.03403403 14.01401401\n",
      " 13.99399399 13.97397397 13.95395395 13.93393393 13.91391391 13.89389389\n",
      " 13.87387387 13.85385385 13.83383383 13.81381381 13.79379379 13.77377377\n",
      " 13.75375375 13.73373373 13.71371371 13.69369369 13.67367367 13.65365365\n",
      " 13.63363363 13.61361361 13.59359359 13.57357357 13.55355355 13.53353353\n",
      " 13.51351351 13.49349349 13.47347347 13.45345345 13.43343343 13.41341341\n",
      " 13.39339339 13.37337337 13.35335335 13.33333333 13.31331331 13.29329329\n",
      " 13.27327327 13.25325325 13.23323323 13.21321321 13.19319319 13.17317317\n",
      " 13.15315315 13.13313313 13.11311311 13.09309309 13.07307307 13.05305305\n",
      " 13.03303303 13.01301301 12.99299299 12.97297297 12.95295295 12.93293293\n",
      " 12.91291291 12.89289289 12.87287287 12.85285285 12.83283283 12.81281281\n",
      " 12.79279279 12.77277277 12.75275275 12.73273273 12.71271271 12.69269269\n",
      " 12.67267267 12.65265265 12.63263263 12.61261261 12.59259259 12.57257257\n",
      " 12.55255255 12.53253253 12.51251251 12.49249249 12.47247247 12.45245245\n",
      " 12.43243243 12.41241241 12.39239239 12.37237237 12.35235235 12.33233233\n",
      " 12.31231231 12.29229229 12.27227227 12.25225225 12.23223223 12.21221221\n",
      " 12.19219219 12.17217217 12.15215215 12.13213213 12.11211211 12.09209209\n",
      " 12.07207207 12.05205205 12.03203203 12.01201201 11.99199199 11.97197197\n",
      " 11.95195195 11.93193193 11.91191191 11.89189189 11.87187187 11.85185185\n",
      " 11.83183183 11.81181181 11.79179179 11.77177177 11.75175175 11.73173173\n",
      " 11.71171171 11.69169169 11.67167167 11.65165165 11.63163163 11.61161161\n",
      " 11.59159159 11.57157157 11.55155155 11.53153153 11.51151151 11.49149149\n",
      " 11.47147147 11.45145145 11.43143143 11.41141141 11.39139139 11.37137137\n",
      " 11.35135135 11.33133133 11.31131131 11.29129129 11.27127127 11.25125125\n",
      " 11.23123123 11.21121121 11.19119119 11.17117117 11.15115115 11.13113113\n",
      " 11.11111111 11.09109109 11.07107107 11.05105105 11.03103103 11.01101101\n",
      " 10.99099099 10.97097097 10.95095095 10.93093093 10.91091091 10.89089089\n",
      " 10.87087087 10.85085085 10.83083083 10.81081081 10.79079079 10.77077077\n",
      " 10.75075075 10.73073073 10.71071071 10.69069069 10.67067067 10.65065065\n",
      " 10.63063063 10.61061061 10.59059059 10.57057057 10.55055055 10.53053053\n",
      " 10.51051051 10.49049049 10.47047047 10.45045045 10.43043043 10.41041041\n",
      " 10.39039039 10.37037037 10.35035035 10.33033033 10.31031031 10.29029029\n",
      " 10.27027027 10.25025025 10.23023023 10.21021021 10.19019019 10.17017017\n",
      " 10.15015015 10.13013013 10.11011011 10.09009009 10.07007007 10.05005005\n",
      " 10.03003003 10.01001001  9.98998999  9.96996997  9.94994995  9.92992993\n",
      "  9.90990991  9.88988989  9.86986987  9.84984985  9.82982983  9.80980981\n",
      "  9.78978979  9.76976977  9.74974975  9.72972973  9.70970971  9.68968969\n",
      "  9.66966967  9.64964965  9.62962963  9.60960961  9.58958959  9.56956957\n",
      "  9.54954955  9.52952953  9.50950951  9.48948949  9.46946947  9.44944945\n",
      "  9.42942943  9.40940941  9.38938939  9.36936937  9.34934935  9.32932933\n",
      "  9.30930931  9.28928929  9.26926927  9.24924925  9.22922923  9.20920921\n",
      "  9.18918919  9.16916917  9.14914915  9.12912913  9.10910911  9.08908909\n",
      "  9.06906907  9.04904905  9.02902903  9.00900901  8.98898899  8.96896897\n",
      "  8.94894895  8.92892893  8.90890891  8.88888889  8.86886887  8.84884885\n",
      "  8.82882883  8.80880881  8.78878879  8.76876877  8.74874875  8.72872873\n",
      "  8.70870871  8.68868869  8.66866867  8.64864865  8.62862863  8.60860861\n",
      "  8.58858859  8.56856857  8.54854855  8.52852853  8.50850851  8.48848849\n",
      "  8.46846847  8.44844845  8.42842843  8.40840841  8.38838839  8.36836837\n",
      "  8.34834835  8.32832833  8.30830831  8.28828829  8.26826827  8.24824825\n",
      "  8.22822823  8.20820821  8.18818819  8.16816817  8.14814815  8.12812813\n",
      "  8.10810811  8.08808809  8.06806807  8.04804805  8.02802803  8.00800801\n",
      "  7.98798799  7.96796797  7.94794795  7.92792793  7.90790791  7.88788789\n",
      "  7.86786787  7.84784785  7.82782783  7.80780781  7.78778779  7.76776777\n",
      "  7.74774775  7.72772773  7.70770771  7.68768769  7.66766767  7.64764765\n",
      "  7.62762763  7.60760761  7.58758759  7.56756757  7.54754755  7.52752753\n",
      "  7.50750751  7.48748749  7.46746747  7.44744745  7.42742743  7.40740741\n",
      "  7.38738739  7.36736737  7.34734735  7.32732733  7.30730731  7.28728729\n",
      "  7.26726727  7.24724725  7.22722723  7.20720721  7.18718719  7.16716717\n",
      "  7.14714715  7.12712713  7.10710711  7.08708709  7.06706707  7.04704705\n",
      "  7.02702703  7.00700701  6.98698699  6.96696697  6.94694695  6.92692693\n",
      "  6.90690691  6.88688689  6.86686687  6.84684685  6.82682683  6.80680681\n",
      "  6.78678679  6.76676677  6.74674675  6.72672673  6.70670671  6.68668669\n",
      "  6.66666667  6.64664665  6.62662663  6.60660661  6.58658659  6.56656657\n",
      "  6.54654655  6.52652653  6.50650651  6.48648649  6.46646647  6.44644645\n",
      "  6.42642643  6.40640641  6.38638639  6.36636637  6.34634635  6.32632633\n",
      "  6.30630631  6.28628629  6.26626627  6.24624625  6.22622623  6.20620621\n",
      "  6.18618619  6.16616617  6.14614615  6.12612613  6.10610611  6.08608609\n",
      "  6.06606607  6.04604605  6.02602603  6.00600601  5.98598599  5.96596597\n",
      "  5.94594595  5.92592593  5.90590591  5.88588589  5.86586587  5.84584585\n",
      "  5.82582583  5.80580581  5.78578579  5.76576577  5.74574575  5.72572573\n",
      "  5.70570571  5.68568569  5.66566567  5.64564565  5.62562563  5.60560561\n",
      "  5.58558559  5.56556557  5.54554555  5.52552553  5.50550551  5.48548549\n",
      "  5.46546547  5.44544545  5.42542543  5.40540541  5.38538539  5.36536537\n",
      "  5.34534535  5.32532533  5.30530531  5.28528529  5.26526527  5.24524525\n",
      "  5.22522523  5.20520521  5.18518519  5.16516517  5.14514515  5.12512513\n",
      "  5.10510511  5.08508509  5.06506507  5.04504505  5.02502503  5.00500501\n",
      "  4.98498498  4.96496496  4.94494494  4.92492492  4.9049049   4.88488488\n",
      "  4.86486486  4.84484484  4.82482482  4.8048048   4.78478478  4.76476476\n",
      "  4.74474474  4.72472472  4.7047047   4.68468468  4.66466466  4.64464464\n",
      "  4.62462462  4.6046046   4.58458458  4.56456456  4.54454454  4.52452452\n",
      "  4.5045045   4.48448448  4.46446446  4.44444444  4.42442442  4.4044044\n",
      "  4.38438438  4.36436436  4.34434434  4.32432432  4.3043043   4.28428428\n",
      "  4.26426426  4.24424424  4.22422422  4.2042042   4.18418418  4.16416416\n",
      "  4.14414414  4.12412412  4.1041041   4.08408408  4.06406406  4.04404404\n",
      "  4.02402402  4.004004    3.98398398  3.96396396  3.94394394  3.92392392\n",
      "  3.9039039   3.88388388  3.86386386  3.84384384  3.82382382  3.8038038\n",
      "  3.78378378  3.76376376  3.74374374  3.72372372  3.7037037   3.68368368\n",
      "  3.66366366  3.64364364  3.62362362  3.6036036   3.58358358  3.56356356\n",
      "  3.54354354  3.52352352  3.5035035   3.48348348  3.46346346  3.44344344\n",
      "  3.42342342  3.4034034   3.38338338  3.36336336  3.34334334  3.32332332\n",
      "  3.3033033   3.28328328  3.26326326  3.24324324  3.22322322  3.2032032\n",
      "  3.18318318  3.16316316  3.14314314  3.12312312  3.1031031   3.08308308\n",
      "  3.06306306  3.04304304  3.02302302  3.003003    2.98298298  2.96296296\n",
      "  2.94294294  2.92292292  2.9029029   2.88288288  2.86286286  2.84284284\n",
      "  2.82282282  2.8028028   2.78278278  2.76276276  2.74274274  2.72272272\n",
      "  2.7027027   2.68268268  2.66266266  2.64264264  2.62262262  2.6026026\n",
      "  2.58258258  2.56256256  2.54254254  2.52252252  2.5025025   2.48248248\n",
      "  2.46246246  2.44244244  2.42242242  2.4024024   2.38238238  2.36236236\n",
      "  2.34234234  2.32232232  2.3023023   2.28228228  2.26226226  2.24224224\n",
      "  2.22222222  2.2022022   2.18218218  2.16216216  2.14214214  2.12212212\n",
      "  2.1021021   2.08208208  2.06206206  2.04204204  2.02202202  2.002002\n",
      "  1.98198198  1.96196196  1.94194194  1.92192192  1.9019019   1.88188188\n",
      "  1.86186186  1.84184184  1.82182182  1.8018018   1.78178178  1.76176176\n",
      "  1.74174174  1.72172172  1.7017017   1.68168168  1.66166166  1.64164164\n",
      "  1.62162162  1.6016016   1.58158158  1.56156156  1.54154154  1.52152152\n",
      "  1.5015015   1.48148148  1.46146146  1.44144144  1.42142142  1.4014014\n",
      "  1.38138138  1.36136136  1.34134134  1.32132132  1.3013013   1.28128128\n",
      "  1.26126126  1.24124124  1.22122122  1.2012012   1.18118118  1.16116116\n",
      "  1.14114114  1.12112112  1.1011011   1.08108108  1.06106106  1.04104104\n",
      "  1.02102102  1.001001    0.98098098  0.96096096  0.94094094  0.92092092\n",
      "  0.9009009   0.88088088  0.86086086  0.84084084  0.82082082  0.8008008\n",
      "  0.78078078  0.76076076  0.74074074  0.72072072  0.7007007   0.68068068\n",
      "  0.66066066  0.64064064  0.62062062  0.6006006   0.58058058  0.56056056\n",
      "  0.54054054  0.52052052  0.5005005   0.48048048  0.46046046  0.44044044\n",
      "  0.42042042  0.4004004   0.38038038  0.36036036  0.34034034  0.32032032\n",
      "  0.3003003   0.28028028  0.26026026  0.24024024  0.22022022  0.2002002\n",
      "  0.18018018  0.16016016  0.14014014  0.12012012  0.1001001   0.08008008\n",
      "  0.06006006  0.04004004  0.02002002  0.        ]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
      "  data[\"rt\"].iloc[:1000] = np.linspace(20, 0, 1000)\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_25238/1241343318.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"choice\"].iloc[:1000] = -1\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_25238/1241343318.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"choice\"].iloc[1000:] = 1\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.zeros((2000, 7), dtype=np.float32),\n",
    "    columns=[\"v\", \"a\", \"z\", \"t\", \"theta\", \"rt\", \"choice\"],\n",
    ")\n",
    "data[\"v\"] = 0.5\n",
    "data[\"a\"] = 0.75\n",
    "data[\"z\"] = 0.5\n",
    "data[\"t\"] = 0.2\n",
    "data[\"theta\"] = 0.1\n",
    "data[\"rt\"].iloc[:1000] = np.linspace(20, 0, 1000)\n",
    "data[\"rt\"].iloc[1000:] = np.linspace(0, 20, 1000)\n",
    "data[\"choice\"].iloc[:1000] = -1\n",
    "data[\"choice\"].iloc[1000:] = 1\n",
    "\n",
    "# Network predictions\n",
    "predict_on_batch_out = network.predict_on_batch(data.values.astype(np.float32))\n",
    "\n",
    "# Simulations\n",
    "from ssms.basic_simulators.simulator import simulator\n",
    "\n",
    "sim_out = simulator(model=\"angle\", theta=data.values[0, :-2], n_samples=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'likelihod')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot network predictions\n",
    "plt.plot(\n",
    "    data[\"rt\"] * data[\"choice\"],\n",
    "    predict_on_batch_out,\n",
    "    color=\"black\",\n",
    "    label=\"network\",\n",
    ")\n",
    "\n",
    "# Plot simulations\n",
    "# plt.hist(\n",
    "#     sim_out[\"rts\"] * sim_out[\"choices\"],\n",
    "#     bins=100,\n",
    "#     histtype=\"step\",\n",
    "#     label=\"simulations\",\n",
    "#     color=\"blue\",\n",
    "#     density=True,\n",
    "# )\n",
    "plt.legend()\n",
    "plt.title(\"SSM likelihood\")\n",
    "plt.xlabel(\"rt\")\n",
    "plt.ylabel(\"likelihod\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hope this package may be helpful in case you attempt to train [LANs](https://elifesciences.org/articles/65074) for your own research."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('cssm')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c2404e761a8d4e2a34f63613cf4c9a9997cd3109cabb959a7904b2035989131a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
